Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild

TL;DR

This paper introduces an illumination-invariant active camera relocalization method that enhances pose estimation robustness under varying lighting conditions, enabling more reliable fine-grained change detection in outdoor scenes.

Contribution

It proposes a novel approach using plane segments and a linear system to improve relocalization accuracy and speed, especially in outdoor environments with lighting variations.

Findings

Achieves nearly 1.6 times faster relocalization than state-of-the-art methods.

Demonstrates robustness and effectiveness in outdoor fine-grained change detection.

Expands applicability of change monitoring for cultural heritage preservation.

Abstract

Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD). Despite the fruitful achievements that ACR can support, it still remains a challenging problem caused by the unstable results of relative pose estimation, especially for outdoor scenes, where the lighting condition is out of control, i.e., the twice observations may have highly varied illuminations. This paper studies an illumination-invariant active camera relocalization method, it improves both in relative pose estimation and scale estimation. We use plane segments as an intermediate representation to facilitate feature matching, thus further boosting pose estimation robustness and reliability under lighting variances. Moreover, we construct a linear system to obtain the absolute scale in each ACR iteration by minimizing the image warping error, thus, significantly reduce the time consume of ACR process, it is nearly $1.6$ times faster than the state-of-the-art ACR strategy. Our work greatly expands the feasibility of real-world fine-grained change monitoring tasks for cultural heritages. Extensive experiments tests and real-world applications verify the effectiveness and robustness of the proposed pose estimation method using for ACR tasks.